In understanding how ring channels form and maintain in intracellular transport, I am also developing data analysis methods to connect simulation and experimental outputs. In particular, I am using tools from topological data analysis to analyze time-series data from agent-based simulations. Using persistent homology, we can assess the impact of simulation parameters on significant topological ring openings.

Collaborators: Adriana Dawes (OSU), Scott McKinley (Tulane University), Riley Juenemann (Tulane University).
Preprints:

1. MV Ciocanel, R. Juenemann, A Dawes, S McKinley. Topological data analysis approaches to uncovering the timing of ring structure onset in filamentous networks (In revision). arXiv preprint .

Talk: Workshop on Mathematical and Computational Methods in Biology at MBI, OSU

Interactions between individual agents can lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. One example is the Kuramoto model, a canonical representation of coupled oscillators that attempt to synchronize.

With collaborators from the Mathematics Research Communities workshop in summer 2018, we are developing methods to infer the underlying interaction network given the time-series dynamics of a coupled phase-oscillator system. Given sufficient observation data on the transient evolution of each oscillator, we use machine learning methods to reconstruct the interaction network and identify model parameters.

Collaborators: Lauren Lazarus (Trinity College), Mark Panaggio (Hillsdale College), Chad Topaz (Williams College), Bin Xu (University of Notre Dame).
Publications:

1. MJ Panaggio, MV Ciocanel, L Lazarus, CM Topaz, B Xu. Model reconstruction from temporal data for coupled oscillator networks. Chaos 29 (2019): 113-116. DOI , Arxiv.

We are interested in quantitatively studying federal sentencing disparities, both at the level of factors that influence judges’ sentencing as well as by studying sentencing equity and patterns of individual judges. We are carrying out merging of datasets that record judge information for individual offenders/cases with sentencing commision data. To establish random assignment of cases, we will use relevant statistical tests and Monte Carlo simulations. This project reflects efforts by the Institute for the Quantitative Study of Inclusion, Diversity, and Equity (QSIDE) to use data science in promoting social justice.

Collaborators: Rebecca Santorella (Brown University), Shilad Sen (Macalester College), Christian Smith (University of Wisconsin-Madison), Chad Topaz (Williams College).